bayesvalidrox.surrogate_models

Note classes that should be visible from the outside.

Modules

apoly_construction	Construction of polynomials for aPCE
bayes_linear	Classes for Bayesian Regression
engine	Engine to train the surrogate
eval_rec_rule	Based on the implementation in UQLab [1].
exp_designs	Experimental design with associated sampling methods
exploration	Exploration for sequential training of metamodels
gaussian_process_sklearn	Implementation of metamodel as GPE, using the Scikit-Learn library
glexindex	Multi indices for monomial exponents.
input_space	Input space built from set prior distributions
inputs	Inputs and related marginal distributions
meta_model	Implementation of metamodel as either PC, aPC or GPE
orthogonal_matching_pursuit	Class OrthogonalMatchingPursuit, inherits from scikit
pce_gpr	Implementation of metamodel as combination of PC + GPE.
polynomial_chaos	Implementation of metamodel as PC or aPC
reg_fast_ard	Class RegressionFastARD, inherits from scikit
reg_fast_laplace	Implementation of FastLaplace in scikit style.
sequential_design	Engine to train the surrogate
supplementary	Supplementary functions that are used in multiple classes